Predicting Sick Patient Volume in a Pediatric Outpatient Setting using Time Series Analysis

Grace Guan, Barbara E. Engelhardt
Proceedings of the 4th Machine Learning for Healthcare Conference, PMLR 106:271-287, 2019.

Abstract

Reducing patients’ medical wait times by improving resource and staffing allocation is an important area of focus in hospital operations management. Two ways to decrease wait times are to adjust staffing or to limit the number of non-urgent visits to reflect a predicted volume of sick patients. Currently, this problem has been approached by both generalized linear models and time series models, and has mainly been researched in the context of adult emergency departments. We analyze sick visit data over a nine year period from one pediatric group (PG) that serves over 30,000 sick infants, children, and adolescents yearly in a walk-in and appointment-based out-patient clinic. The PG currently schedules staff and well-child appointments assuming a constant number of sick visits daily despite weekly and seasonal cycles in the data. We develop time series models to estimate the volume of sick patients that the PG can expect on any given day, so that clinicians can be allocated and the number of well-child appointments scheduled in advance can be adjusted according to predictions. First, we find that recurrent neural network (RNN) models are able to capture the seasonality of the data and perform substantially better than state-of-the-art models, including constant predictions. Next, we find that previous days’ data can be used to perform outbreak detection by identifying error outliers. Lastly, we find improvements in prediction when modeling sick patients as a mixture of disease types, because disease types are concentrated differently throughout the year. Resource allocation based on these findings can be expanded upon to reduce wait time by improving staffing at pediatric emergency departments and outpatient clinics.

Cite this Paper

BibTeX
@InProceedings{pmlr-v106-guan19a, title = {Predicting Sick Patient Volume in a Pediatric Outpatient Setting using Time Series Analysis}, author = {Guan, Grace and Engelhardt, Barbara E.}, booktitle = {Proceedings of the 4th Machine Learning for Healthcare Conference}, pages = {271--287}, year = {2019}, editor = {Doshi-Velez, Finale and Fackler, Jim and Jung, Ken and Kale, David and Ranganath, Rajesh and Wallace, Byron and Wiens, Jenna}, volume = {106}, series = {Proceedings of Machine Learning Research}, month = {09--10 Aug}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v106/guan19a/guan19a.pdf}, url = {https://proceedings.mlr.press/v106/guan19a.html}, abstract = {Reducing patients’ medical wait times by improving resource and staffing allocation is an important area of focus in hospital operations management. Two ways to decrease wait times are to adjust staffing or to limit the number of non-urgent visits to reflect a predicted volume of sick patients. Currently, this problem has been approached by both generalized linear models and time series models, and has mainly been researched in the context of adult emergency departments. We analyze sick visit data over a nine year period from one pediatric group (PG) that serves over 30,000 sick infants, children, and adolescents yearly in a walk-in and appointment-based out-patient clinic. The PG currently schedules staff and well-child appointments assuming a constant number of sick visits daily despite weekly and seasonal cycles in the data. We develop time series models to estimate the volume of sick patients that the PG can expect on any given day, so that clinicians can be allocated and the number of well-child appointments scheduled in advance can be adjusted according to predictions. First, we find that recurrent neural network (RNN) models are able to capture the seasonality of the data and perform substantially better than state-of-the-art models, including constant predictions. Next, we find that previous days’ data can be used to perform outbreak detection by identifying error outliers. Lastly, we find improvements in prediction when modeling sick patients as a mixture of disease types, because disease types are concentrated differently throughout the year. Resource allocation based on these findings can be expanded upon to reduce wait time by improving staffing at pediatric emergency departments and outpatient clinics.} }
Endnote
%0 Conference Paper %T Predicting Sick Patient Volume in a Pediatric Outpatient Setting using Time Series Analysis %A Grace Guan %A Barbara E. Engelhardt %B Proceedings of the 4th Machine Learning for Healthcare Conference %C Proceedings of Machine Learning Research %D 2019 %E Finale Doshi-Velez %E Jim Fackler %E Ken Jung %E David Kale %E Rajesh Ranganath %E Byron Wallace %E Jenna Wiens %F pmlr-v106-guan19a %I PMLR %P 271--287 %U https://proceedings.mlr.press/v106/guan19a.html %V 106 %X Reducing patients’ medical wait times by improving resource and staffing allocation is an important area of focus in hospital operations management. Two ways to decrease wait times are to adjust staffing or to limit the number of non-urgent visits to reflect a predicted volume of sick patients. Currently, this problem has been approached by both generalized linear models and time series models, and has mainly been researched in the context of adult emergency departments. We analyze sick visit data over a nine year period from one pediatric group (PG) that serves over 30,000 sick infants, children, and adolescents yearly in a walk-in and appointment-based out-patient clinic. The PG currently schedules staff and well-child appointments assuming a constant number of sick visits daily despite weekly and seasonal cycles in the data. We develop time series models to estimate the volume of sick patients that the PG can expect on any given day, so that clinicians can be allocated and the number of well-child appointments scheduled in advance can be adjusted according to predictions. First, we find that recurrent neural network (RNN) models are able to capture the seasonality of the data and perform substantially better than state-of-the-art models, including constant predictions. Next, we find that previous days’ data can be used to perform outbreak detection by identifying error outliers. Lastly, we find improvements in prediction when modeling sick patients as a mixture of disease types, because disease types are concentrated differently throughout the year. Resource allocation based on these findings can be expanded upon to reduce wait time by improving staffing at pediatric emergency departments and outpatient clinics.
APA
Guan, G. & Engelhardt, B.E.. (2019). Predicting Sick Patient Volume in a Pediatric Outpatient Setting using Time Series Analysis. Proceedings of the 4th Machine Learning for Healthcare Conference, in Proceedings of Machine Learning Research 106:271-287 Available from https://proceedings.mlr.press/v106/guan19a.html.

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